code: def find_homography_dlt_iterated(
      points1: Tensor,
      points2: Tensor,
      weights: Tensor,
      soft_inl_th: float = 3.0,
      n_iter: int = 5,
  ) -> Tensor:
      r"""Compute the homography matrix using the iteratively-reweighted least squares (IRWLS).
  
      The linear system is solved by using the Reweighted Least Squares Solution for the 4 Points algorithm.
  
      Args:
          points1: A set of points in the first image with a tensor shape :math:`(B, N, 2)`.
          points2: A set of points in the second image with a tensor shape :math:`(B, N, 2)`.
          weights: Tensor containing the weights per point correspondence with a shape of :math:`(B, N)`.
            Used for the first iteration of the IRWLS.
          soft_inl_th: Soft inlier threshold used for weight calculation.
          n_iter: number of iterations.
  
      Returns:
          the computed homography matrix with shape :math:`(B, 3, 3)`.
      """
      H: Tensor = find_homography_dlt(points1, points2, weights)
      for _ in range(n_iter - 1):
          errors: Tensor = symmetric_transfer_error(
              points1, points2, H, False
          )
          weights_new: Tensor = torch.exp(
              -errors / (2.0 * (soft_inl_th**2))
          )
          H = find_homography_dlt(points1, points2, weights_new)
      return H
  
file_path: kornia/kornia/geometry/homography.py
imports: import warnings
  from typing import Optional, Tuple
  import torch
  from kornia.core import Tensor
  from kornia.core.check import KORNIA_CHECK_SHAPE
  from kornia.utils import _extract_device_dtype, safe_inverse_with_mask, safe_solve_with_mask
  from kornia.utils.helpers import _torch_svd_cast
  from .conversions import convert_points_from_homogeneous, convert_points_to_homogeneous
  from .epipolar import normalize_points
  from .linalg import transform_points
label_desc: Use image node_id 5 for calling a global function with example usage: find_homography_dlt_iterated(points1, points2, weights, soft_inl_th, n_iter) and returns: H